Experiments seek to investigate and quantify the computational efficiency and the descriptor matching accuracy, based on 3D brain volumes. Computational efficiency is assessed in terms of mean computation time for 3D SIFT feature extraction from individual images, for generating three types of descriptors: the original SIFT-Rank [1]} and novel BRIEF and RRIEF descriptors proposed here. Descriptor accuracy is assessed via the sum of matching features resulting from nearest neighbor descriptor matching trials between keypoints extracted in pairs of brain volumes. A variant of the Hough transform to identify a robust 7 degree-of-freedom transform (i.e. 3D rotation, translation and isotropic scale) aligning pairs of 3D volumes [1]}, matching keypoint pairs are identified as inliers to the global Hough transform. Matching results are provided for proposed BRIEF and RRIEF descriptor parameters including 5 normalization strategies and 10 different sigma values.
